Predictive Analysis and Data Science For Big Data

Predictive analysis gives organizations full investigative power to delve into any corner of data to discover otherwise obscure details behind specific performance outcomes. The main idea of predictive analysis is to use current and past data to predict future events. The goal of the statistical techniques used in predictive analysis is to determine market patterns, identify risks, and predict potential opportunities for growth. In addition, data relationships can be reordered to determine the most plausible outcome of possible solutions and patterns can be recognized that might have the power to alter the outcome of a probable event.

Eric Robson, leader of the Data Mining and Social Networks Analysis Group at the TSSG, a part of Ireland’s Waterford Institute of Technology, explains: “For instance, a large supermarket has many thousands of customers and many thousands of products to sell. Usually each customer is tracked via their charge card … and we are able to see return visits. On day one, a customer might buy bread and some butter. On day three, they buy some more bread, but it might not be until day fourteen that they need to buy some more butter. From this simple example we can see how a trend or a purchasing pattern can be determined.” This, then, is predictive analysis.

Robson continues: “In social network predictive analytics people are constantly passing messages to each other. From a marketing perspective, we can look at who we should be targeting to send our viral message out to for further [propagation.] Who is the biggest distributors of content? It may not necessarily be commercial entities. It could be; bloggers, people with very active Facebook accounts, people with very active Twitter accounts. In terms of product, we can start identifying who are the key influencers. Say, IOracle Advanced Analytics Option wanted to sell something like running shoes and this guy is a marathon runner and blogs about them. If we know that people listen to him, then the running shoe manufacturer can start targeting this guy. ‘Here’s a free pair of running shoes. Tell us what you think of them.’ More importantly, ‘Tell the world what you think of them.'”

In the past, marketers would use relatively small numbers to extrapolate a larger result. With social media, they can look at thousands or millions of opinions and come to conclusions that lead to refreshing existing campaigns or creating new ones. They can analyze raw consumer opinion at its source – this more likely to reveal the unvarnished truth, unlike the sometimes false positives often derived via focus groups and surveys.

Numerous firms offer superior software applications which make predictive analysis a relatively easy task – at least from a technical viewpoint.


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